caa a22 4500 606160213 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1795-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1795-6 Parameter extraction of solar cell models using chaotic asexual reproduction optimization [Elektronische Daten] [Xiaofang Yuan, Yuqing He, Liangjiang Liu] To simulate solar cell systems or to optimize photovoltaic (PV) system performance, the estimation of solar cell model parameters is extremely crucial. In this paper, the parameter extraction of solar cell models is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured data. A novel chaotic asexual reproduction optimization (CARO) using chaotic sequence for global search is applied to this parameter extraction problem. All the seven or five parameters of solar cell models are extracted simultaneously using measured input-output data. The CARO has been tested with different solar cell models, i.e., double diode, single diode, and PV module. Comparison simulation results with other parameter extraction techniques show that the CARO signifies its potential as another optional method. The Natural Computing Applications Forum, 2014 Parameter extraction nationallicence Solar cell models nationallicence Chaotic asexual reproduction optimization (CARO) nationallicence Chaotic sequence nationallicence Yuan Xiaofang College of Electrical and Information Engineering, Hunan University, 410082, Hunan, China aut He Yuqing College of Electrical and Information Engineering, Hunan University, 410082, Hunan, China aut Liu Liangjiang Hunan Institute of Metrology and Test, 410014, Changsha, China aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1227-1239 0941-0643 26:5<1227 2015 26 521 https://doi.org/10.1007/s00521-014-1795-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1795-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yuan Xiaofang College of Electrical and Information Engineering, Hunan University, 410082, Hunan, China aut NATIONALLICENCE 700 1- He Yuqing College of Electrical and Information Engineering, Hunan University, 410082, Hunan, China aut NATIONALLICENCE 700 1- Liu Liangjiang Hunan Institute of Metrology and Test, 410014, Changsha, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1227-1239 0941-0643 26:5<1227 2015 26 521